ASA DataFest 2024 Workshop

Contents

• Data Visualisation with ggplot2
• Combine the skills learnt so far.

Acknowledgement

• The content in this module are heavily inspired by the following work:
  • Emi Tanaka’s Data Visualisation with R workshop.
  • Alison Hill’s presentation: “Plot Twist. 10 Bake Offs, 11 Ways”.

If you have done this workshop/STAT1378 before

Constructing plots with R: base version

df

# # A tibble: 3 × 2
#   duty      perc
#   <chr>    <dbl>
# 1 Teaching    40
# 2 Research    40
# 3 Admin       20

• Single purpose functions to generate “named plots”

barplot(as.matrix(df$perc),
  legend = df$duty
)

pie(df$perc, labels = df$duty)

Data Visualisation with ggplot2.

• R has several systems for making graphs, but ggplot2 of Wickham et al. (2022) is one of the most elegant and most versatile.
• ggplot2 implements the grammar of graphics, a coherent system for describing and building graphs.

• With ggplot2, you can do more faster by learning one system and applying it in many places.

A graphing template

• Let’s turn this code into a reusable template for making graphs with ggplot2.
• To make a graph, replace the bracketed sections in the code below with a dataset, a geom function, or a collection of mappings.

ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

Palmer penguins

Palmer penguins (cont.)

# pak::pak("palmerpenguins") # if not installed or
# install.packages("palmerpenguins")
library(tidyverse)
library(palmerpenguins)
glimpse(penguins)

# Rows: 344
# Columns: 8
# $ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
# $ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
# $ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
# $ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
# $ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
# $ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
# $ sex               <fct> male, female, female, NA, female, male, female, male…
# $ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

• For example:

library(tidyverse)
ggplot(
  data = penguins,
  mapping = aes(
    x = bill_depth_mm,
    y = bill_length_mm,
    colour = species
  )
) +
  geom_point()

Aestnestic mappings

Hidden argument names in ggplot

• No need to write out explicitly data =, mapping =, x =, and y = each time in ggplot.
• ggplot code in the wild often omit these argument names.
• But position needs to be correct if argument names are not specified!

Hidden argument names in ggplot

For example:

ggplot(
  penguins,
  aes(species)
)

ggplot(
  penguins,
  aes(species, bill_length_mm)
)

Different geometric objects:

p <- ggplot(penguins, aes(species, bill_length_mm))

p + geom_boxplot()

p + geom_point()

geom

• Here is a list of some commonly used geom:

Statistical transformation

g <- ggplot(penguins, aes(species, bill_length_mm)) +
  geom_boxplot()

• Notice the y-axis is not the raw data!
• It is plotting a statistical transformation of the y-values.
• Under the hood, data is transformed (including x factor input to numerical values).

Add multiple layers

Add multiple layers (cont.)

• Each layer inherits mapping and data from ggplot by default.

ggplot(penguins, 
       aes(x = species, 
           y = bill_length_mm)
       ) +
  geom_violin() +
  geom_boxplot() +
  geom_point()

Order of the layers matters!

• Boxplot and violin plot order are switched around.

ggplot(
  penguins,
  aes(species, bill_length_mm)
) +
  geom_violin() +
  geom_boxplot() +
  geom_point()

ggplot(
  penguins,
  aes(species, bill_length_mm)
) +
  geom_boxplot() +
  geom_violin() +
  geom_point()

Layer-specific data and aesthetic mappings

Layer-specific data and aesthetic mappings (cont.)

• For each layer, aesthetic and/or data can be overwritten.

ggplot(penguins, aes(species, bill_length_mm)) +
  geom_violin(aes(fill = species)) +
  geom_boxplot(data = filter(penguins, species == "Adelie")) +
  geom_point(
    data = filter(penguins, species == "Gentoo"),
    aes(y = bill_depth_mm)
  )

Aesthestic or Attribute?

ggplot(penguins) +
  geom_point(aes(body_mass_g,
    bill_depth_mm,
    colour = "blue"
  ))

Aesthestic or Attribute?

ggplot(penguins) +
  geom_point(aes(body_mass_g,
    bill_depth_mm,
    colour = "blue"
  ))

ggplot(penguins) +
  geom_point(aes(
    body_mass_g,
    bill_depth_mm
  ),
  colour = "blue"
  )

group in ggplot

ggplot(
  penguins,
  aes(
    body_mass_g,
    bill_depth_mm
  )
) +
  geom_point(aes(colour = species)) +
  geom_smooth(method = "lm")

• What if we want to draw the fit of a simple linear model for each cluster?

group in ggplot (cont.)

ggplot(
  penguins,
  aes(
    body_mass_g,
    bill_depth_mm
  )
) +
  geom_point(aes(colour = species)) +
  geom_smooth(
    method = "lm",
    aes(group = species == "Gentoo")
  )

geom_smooth(method = "lm", aes(group = species))

Facetting

g <- ggplot(
  penguins,
  aes(bill_length_mm, bill_depth_mm, 
      colour = species)
) +
  geom_point()
g

Facetting (cont.)

• If you have only one variable with many levels:

g + facet_wrap(~sex)

Facetting (cont.)

• If you have two discrete variables

g + facet_grid(island ~ sex)

Your mission, should you choose to accept it!

Recipe 1: Questions

Recipe 2: Questions

Recipe 3: Questions

Recipe 4: Questions

Recipe 5: Questions

Recipe 6: Questions

The patchwork package

• The patchwork package of Pedersen (2022) allows us to combine and arrange multiple plots on the same graph.
• See https://patchwork.data-imaginist.com/articles/patchwork.html for the many different ways you can customise the layout of your graphs.

How to get your plots out of R Studio?

• At this stage, these beautiful plots are not much use if they are stuck in RStudio.
• The easiest way to export your plots and save them elsewhere on your computer is by using ggsave().

Think for Yourself, Question Authority

• Visualisation is a powerful tool for communicating data.
• But it can also be used to mislead others.
• What’s wrong with this visualisation?

Think for Yourself, Question Authority (cont.)

• FAFSA = Free Application for Federal Student Aid.
• A form for college students to determine their eligibility for student financial aid in the US.
• What is your first impression this plot?

Think for Yourself, Question Authority (cont.)

• FAFSA = Free Application for Federal Student Aid.
• A form for college students to determine their eligibility for student financial aid in the US.
• But in reality:

Your mission, should you choose to accept it!

• Now it’s your turn to create a few basic plots.
• Try to predict the what the code would do before running them.
  • You can find the code inside module02_exercises.html.
• Just give us a yell if you have any question.

Recipe 1 Plot

# create coordinates for labels
series_labels <- dat1 %>%
  group_by(series) %>%
  summarize(
    y_position = median(viewers_7day) + 1,
    x_position = mean(episode_count)
  )
# make the plot
ggplot(dat1, aes(x = episode_count, y = viewers_7day, fill = series)) +
  geom_col(alpha = .9) +
  ggtitle("Series 8 was a Big Setback in Viewers",
    subtitle = "7-Day Viewers across All Series/Episodes"
  ) +
  geom_text(data = series_labels, aes(
    label = series,
    x = x_position,
    y = y_position
  )) +
  theme(
    axis.text.x = element_blank(),
    axis.ticks.x = element_blank(),
    axis.title.x = element_blank(),
    panel.grid.major.x = element_blank(),
    panel.grid.minor.x = element_blank()
  ) +
  scale_x_discrete(expand = c(0, 0))

Recipe 2 Plot

line_labels <- ratings %>%
  group_by(series) %>%
  mutate(episode = as.numeric(episode)) %>%
  slice_tail(n = 1) %>%
  select(series, x_position = episode, y_position = viewers_7day)

ggplot(ratings, aes(
  x = as.numeric(episode),
  y = viewers_7day,
  colour = series,
  group = series
)) +
  geom_line() +
  labs(colour = "Series", x = "Episode") +
  geom_text(data = line_labels, aes(
    label = series,
    x = x_position + .25,
    y = y_position
  ))

Recipe 3 Plot

ggplot(ratings, aes(
  x = episode,
  y = viewers_7day,
  colour = fct_reorder2(series, episode, viewers_7day),
  group = series
)) +
  facet_wrap(~series) +
  geom_line(lwd = 2) +
  labs(colour = "Series", x = "Episode")

Recipe 4 Plot

# code for plot
ggplot(dat2, aes(
  x = series,
  y = viewers_7day,
  colour = fct_reorder2(episode, series, viewers_7day),
  group = episode
)) +
  geom_point() +
  geom_line() +
  ggtitle("Great British Bake Off Finales Get More Viewers than Premieres") +
  labs(colour = "Episode")

Recipe 5 Plot

slope_labels <- dat2 %>%
  filter(episode == "last") %>%
  select(series, x_position = episode, y_position = viewers_7day)
ggplot(dat2,
  aes(
    x = episode,
    y = viewers_7day,
    colour = series,
    group = series
  )
) +
  geom_point() +
  geom_line() +
  geom_text(
    data = slope_labels, aes(
      label = series,
      x = x_position,
      y = y_position
    ),
    nudge_x = .1
  ) +
  theme(
    panel.grid = element_blank(),
    axis.line = element_line(colour = "gray")
  )

Recipe 6 Plot

# plot
ggplot(dat3, aes(
  x = fct_rev(series),
  y = finale_bump
)) + 
  geom_col(alpha = .7) +
  coord_flip() +
  labs(x = "Series", y = "Difference in Viewers for Finale from Premiere (millions)") +
  ggtitle("Finale 'Bumps' were Smallest for Series 10",
    subtitle = "Finale 7-day Viewers Relative to Premiere"
  )

Skills

• Data visualisation with ggplot2
• Combine the skills learnt so far.